Effect of "magic chlorine" in drug discovery: an in silico approach.

The chlorine atom plays a vital role in drug design, yet the benefits of chlorine in 250 FDA-approved chlorine-containing drugs have not been studied properly. To see the "magic chloro" effect, computational studies have been carried out for 35 inhibitors, which are numbered as 12 complexes with (parent (-H), one chlorine, or two chlorine) substituents. The physicochemical properties are studied by conceptual density functional theory (CDFT). The pharmacokinetics, toxicity and metabolic properties of the studied inhibitors are estimated using chemoinformatics tools. SwissTargetPrediction is used to predict the multitarget activities of the studied inhibitors. Four FDA-approved drugs, diazepam, chloroquine, chloramphenicol, and bendamustine, are referenced to validate the studies. A higher HOMO-LUMO gap predicted high stability for the studied one and two chlorine-substituted analogues. Most of the studied inhibitors show "drug likeliness", nontoxicity, and high gastrointestinal (GI) absorption. The addition of one or two chloro substituents has increased the physicochemical properties and stability of most of the inhibitors compared to the parent analogues, whereas the toxicity is not affected. No change in metabolic properties is observed on addition of one or two chlorine substituents. The multi-target activities of all the studied inhibitors are validated by the reference drugs and experimental results.

Applications of artificial intelligence multiomics in precision oncology.

Cancer is the second leading worldwide disease that depends on oncogenic mutations and non-mutated genes for survival. Recent advancements in next-generation sequencing (NGS) have transformed the health care sector with big data and machine learning (ML) approaches. NGS data are able to detect the abnormalities and mutations in the oncogenes. These multi-omics analyses are used for risk prediction, early diagnosis, accurate prognosis, and identification of biomarkers in cancer patients. The availability of these cancer data and their analysis may provide insights into the biology of the disease, which can be used for the personalized treatment of cancer patients. Bioinformatics tools are delivering this promise by managing, integrating, and analyzing these complex datasets. The clinical outcomes of cancer patients are improved by the use of various innovative methods implicated particularly for diagnosis and therapeutics. ML-based artificial intelligence (AI) applications are solving these issues to a great extent. AI techniques are used to update the patients on a personalized basis about their treatment procedures, progress, recovery, therapies used, dietary changes in lifestyles patterns along with the survival summary of previously recovered cancer patients. In this way, the patients are becoming more aware of their diseases and the entire clinical treatment procedures. Though the technology has its own advantages and disadvantages, we hope that the day is not so far when AI techniques will provide personalized treatment to cancer patients tailored to their needs in much quicker ways.

Role of smartphone devices in precision oncology.

BACKGROUND: To improve the care for cancer patients, personalized treatment including monitoring and managing Quality of life (QoL) data collection of patients in his/her home environment, its integration and its analysis is necessary. Advanced technologies have been used to develop smartphone devices to support cancer patients and clinicians by integrating all patient-relevant data, helping with Patient Reported Outcomes (PRO), side effect management, appointments, and nutritional support. PURPOSE: In this review the role and challenges of using smartphone applications for precision oncology is discussed. METHODS: The methodology section includes the data collection, data integration and predictive modelling approaches. The design, development and evaluation of (AI/ML) algorithms of these apps need intended purpose of these algorithms, description of used mepthods, validity and appropriateness of the datasets, design of the algorithms, evaluation, implementation of these (AI/ML) algorithms and post treatment monitoring. RESULTS: Though Artificial intelligence (AI) based results showed higher diagnostic classification accuracy in most of the results, the advancement of these mobile apps technologies has a few limitations. CONCLUSIONS: ML techniques and DL are used to discover novel biomarkers for early detection and diagnostics, and AI are used to accelerate drug discovery, exploit biomarkers to accurately match patients to clinical trials, and personalize cancer therapy based only on patient's own data. AI based smartphone apps cannot be treated as autonomous rather used as an integrative tool for patient-relevant data, PRO, side effect management, appointments, nutritional support, emotional and social support, severity of pain detection and correct diagnosis at higher level. It should encourage the clinicians and care givers to support and establish patient-physician relationships with the help of these apps.

Tracing the pathways and mechanisms involved in medicinal uses of flaxseed with computational methods and bioinformatics tools.

Pharmacological drugs targeting specific pathways involved in various diseases have seen recent advancement with newer and more efficient emerging drug targets, but these drugs are limited in terms of their side effects and patient adherence. The potential of plant-based diets in the form of functional foods is increasingly being realized as an option to treat and/or prevent several diseases. In this work, we have selected flaxseed (Linum usitatissimum), also known as linseed, to study its pharmacological efficacy and proposed mechanisms of action for medicinal purposes. The target genes of linseed with Disease Specificity Index (DSI >0.6) are compared to the associated genes of diabetes mellitus, decrease in appetite, addictive behavior, cardiovascular diseases (CVDs), inflammatory bowel diseases (IBDs), and Polycystic Ovary Syndrome (PCOS), and the selected genes are further evaluated using in silico methods. The binding affinity of flaxseed to three common target proteins (CCDC28b, PDCD6IP, and USP34) is assessed by docking and molecular dynamics (MD) simulations. The results show that linseed is safe to use for mutagenic toxicity and other cardiotoxicity measures, but linseed is unsafe for embryotoxicity, hERG toxicity, and cardiac failure. The analysis of the protein-protein interaction (PPI) network, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways indicates that flaxseed can be used as a medicinal herb for treatment of diabetes mellitus, cardiovascular diseases, IBDs, and PCOS.

Chemical Reactivity and Optical and Pharmacokinetics Studies of 14 Multikinase Inhibitors and Their Docking Interactions Toward ACK1 for Precision Oncology.

Activated Cdc42-associated kinase 1 (ACK1/TNK2) has a significant role in cell endocytosis, survival, proliferation, and migration. Mutations in ACK1 are closely associated with the occurrence and development of cancers. In this work, a conceptual density functional theory (CDFT)-based computational peptidology (CDFT-CP) method is used to study the chemical reactivity of 14 multikinase inhibitors. Optical properties of these inhibitors are studied by time-dependent density functional theory (TDDFT). Various biological and pharmacokinetic parameters are studied by Osiris, Molinspiration, and BOILED-Egg in SwissADME software tools. Physicochemical and biopharmaceutical (PCB), Salmonella typhimurium reverse mutation assay (AMES) mutagenicity, toxicity, and risk prediction are estimated by Simulations plus ADMET Predictor 10.2 software. MD simulations for an active model of ACK1 is carried out by the CABS-flex 2.0 web server, and potential binding pockets for ACK1 are searched using the PrankWeb server. SwissTargetPrediction is used to predict the potential targets for the multikinase inhibitors. Docking studies are carried out for ACK1-multikinase inhibitors using Autodock 4.2 software. Noncovalent interactions for ACK1-multikinase inhibitor complexes are studied using the Protein-Ligand Interaction Profiler (PLIP) server. Results indicated higher binding affinities and strong noncovalent interactions in ACK1-multikinase inhibitor complexes.

Chemical reactivity and binding interactions in ribonucleic acid-peptide complexes.

The covalent and noncovalent backbone binding interactions in RNA-peptide complexes were studied by DFT methods. Four RNA structures R1(GGCUAGCC), R2(AAUCGAUU), R3(GGGAUCCC), and R4(AAAGCUUU) has been selected for eight protonated peptides (DR, ER, GR, KR, NGR, RR, tmeGnd (tme), and VR) interactions based on an experimental study (Anal Chem. 2019; 91:1659-1664). Chemical reactivity theory is used to study the reactivity of eight peptides with global descriptors. Lower hardness values reflected low stability and high reactivity for the protonated peptides. DR, ER, GR, KR, NGR, RR, and VR show lower value of ω, µ while tme has high value of ω, µ. Larger highest occupied molecular orbital (HOMO)-lowest unoccupied molecular orbital (LUMO) gap for ER, GR, and KR showed greater structural stability for peptides. AutoDock and PatchDock results indicated that R1, R2, and R4 retain hairpin structures while interacting with peptide complexes. The calculated binding energies of (R1-R4)-peptide complexes from AutoDock tools are (1.49-11.12) kcal/mol. Results showed that the noncovalent interactions are stronger than the covalent interactions for R1-peptide complexes. The reason might be the transfer of proton from protonated ligand to deprotonated RNA, which has initiated the loss of the ligand. Also it has been observed that proton transfer has become energetically unfavorable in presence of additional hydrogen bonds which is predicted in the experimental results.

Theoretical Studies on the Molecular Properties, Toxicity, and Biological Efficacy of 21 New Chemical Entities.

New chemical entities (NCEs) such as small molecules and antibody-drug conjugates have strong binding affinity for biological targets, which provide deep insights into structure-specific interactions for the design of future drugs. As structures of drugs increase in complexity, the importance of computational predictions comes into sharp focus. Knowledge of various computational tools enables us to predict the molecular properties, toxicity, and biological efficacy of the drugs and help the medicinal chemists to discover new drugs more efficiently. Newly approved drugs have higher affinities for proteins and nucleic acids and are applied for the treatment of human diseases. We have carried out the computational studies of 21 such NCEs, specifically small molecules and antibody-drug conjugates, and studied the biological efficacy of these drugs. Their bioactivity score and molecular and pharmacokinetic properties were evaluated using online computer software programs, viz., Molinspiration and Osiris Property Explorer. The SwissTargetPrediction tool was used for the efficient prediction of protein targets for the NCEs. The results indicated higher stability for the drug complexes due to a larger HOMO-LUMO gap. A high electrophilicity index reflects good electrophilic behavior and high reactivity of the drugs. Lipinski's ''rule of five'' indicated that most of the drug complexes are likely to be orally active. These drugs also showed non-mutagenic, non-tumorigenic, non-irritant, and non-effective reproductive behavior. We hope that these studies will provide an insight into molecular recognition and definitely help the medicinal chemists to design new drugs in future.

Physicochemical, antioxidant properties of carotenoids and its optoelectronic and interaction studies with chlorophyll pigments.

The physicochemical and antioxidant properties of seven carotenoids: antheraxanthin, ß-carotene, neoxanthin, peridinin, violaxanthin, xanthrophyll and zeaxanthin were studied by theoretical means. Then the Optoelectronic properties and interaction of chlorophyll-carotenoid complexes are analysed by TDDFT and IGMPLOT. Global reactivity descriptors for carotenoids and chlorophyll (Chla, Chlb) are calculated via conceptual density functional theory (CDFT). The higher HOMO-LUMO (HL) gap indicated structural stability of carotenoid, chlorophyll and chlorophyll-carotenoid complexes. The chemical hardness for carotenoids and Chlorophyll is found to be lower in the solvent medium than in the gas phase. Results showed that carotenoids can be used as good reactive nucleophile due to lower µ and ω. As proton affinities (PAs) are much lower than the bond dissociation enthalpies (BDEs), it is anticipated that direct antioxidant activity in these carotenoids is mainly due to the sequential proton loss electron transfer (SPLET) mechanism with dominant solvent effects. Also lower PAs of carotenoid suggest that antioxidant activity by the SPLET mechanism should be a result of a balance between proclivities to transfer protons. Reaction rate constant with Transition-State Theory (TST) were estimated for carotenoid-Chlorophyll complexes in gas phase. Time dependent Density Functional Theory (TDDFT) showed that all the chlorophyll (Chla, Chlb)-carotenoid complexes show absorption wavelength in the visible region. The lower S1-T1 adiabatic energy gap indicated ISC transition from S1 to T1 state.

Application of Optimization Algorithms in Clusters.

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard-Jones (LJ), Born-Mayer, Gupta, Sutton-Chen, and Murrell-Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.

Chemical reactivity theory (CRT) study of small drug-like biologically active molecules.

New biochemical screening and design based technology are used to identify the small molecules in targeting RNA. These approaches has develop potential drug like small molecule for RNA-targeted therapeutics. Chemical Reactivity Theory (CRT) is used to study these drug-like, biologically active small molecules that target RNA. Twenty two small molecules based on structure (1-6), information (7-9), fragment (10-19), small molecular microarrays (20), and use of phenotypic assays (21-22) are selected for the studies of several DFT-based global reactivity and local reactivity descriptors to provide complete explanation for the reactivity of these complexes by chemical reactivity method. Higher HOMO-LUMO gap indicated the structural stability for the studied complexes. The complexes reflect greater thermodynamic stability. Further the results predicted that high aromaticity and hardness are measures of high stability and low reactivity for the studied complexes. It was observed that a good, more reactive, nucleophile can be described by a lower value of µ, ω while a good electrophile can be described by a high value of µ, ω. TDDFT results predicted that few complexes can be used as fluorescent biomarkers as their emission wavelength lies in the visible region. Communicated by Ramaswamy H. Sarma.

Nucleobase Pair-Metal Dimer/Dinuclear Metal Cation Interaction: A Theoretical Study.

Nucleobase pair-metal dimer/dinuclear metal cation interactions play an important role in biological applications because of their highly symmetrical structures and high stabilities. In this work, we have selected five adenine-adenine hydrogen bonding, adenine-thymine (AT), adenine-uracil, adenine-adenine stacking pairs, and Watson-Crick AT stacking pairs and studied their interaction with the coinage metal dimer M2 and M2 2+ metal cations, where M = Ag, Au, and Cu. Quantum chemical calculations have been carried out with density functional theory (DFT) and time-dependent DFT (TDDFT) methods. Electronic structures were analyzed by the partial density of states method. During interactions, we find that M-M distances are shorter than the sum of van der Waals radii of the corresponding two homocoinage metal atoms, which show the existence of significant metallophilic interactions. Results indicated that nucleobase-M2 2+ complexes are stronger as compared to nucleobase-M2 complexes. Also, the replacement of the hydrogen bond by the dinuclear metal cation-coordinated bond forms more stable alternative metallo-DNA sequences in AAST base pairs. TDDFT calculations reveal that nucleobase-Cu2 complexes and nucleobase-Ag2 2+/Au2 2+ complexes can be used for fluorescent markers and logic gate applications. Atom-in-molecules analysis predicted the noncovalent interaction in these complexes.

Interactions, electronic and optical properties of nanographene-peptide complexes: a theoretical study.

We studied the interaction of planar phenylalanine (phe), tryptophan (try), tyrosine (tyr); amide asparagine (asn) and glutamine (gln); arginine (arg) side-chains, charged histidine (his-c) and charged lysine (lys-c) side-chains on a nanographene (g) surface by Density Functional theory (DFT) and Time Dependent Density Functional Theory (TDDFT). The occupied number of states by the system at each energy level and relative contribution of a particular atom/orbital has been studied by Density of States (DOS) and Partial Density of States (PDOS) respectively. Atom-in Molecules (AIM) analysis and non-covalent interaction (NCI) PLOT are used to study the interactions in these complexes. The absorption spectra and HOMO-LUMO (HL) gaps are quantitatively analysed to study the correlation between the optical properties of the studied complexes. The HL gap of peptides is larger than the HL gap of graphene-peptide complexes, indicating strong interactions. All the peptides interact from the above the nanographene surfaces. garg, glys-c, gtry and gtyr complexes have smaller bond distance as compared to gasn, ggln, ghis-c and gphe complexes. AIM analysis and (NCI) PLOT showed noncovalent interactions for these complexes. TDDFT calculations indicated the applicability of these complexes as biosensors.

The Role of Proton Transfer on Mutations.

Hydrogen bonds play a critical role in nucleobase studies as they encode genes, map protein structures, provide stability to the base pairs, and are involved in spontaneous and induced mutations. Proton transfer mechanism is a critical phenomenon that is related to the acid-base characteristics of the nucleobases in Watson-Crick base pairs. The energetic and dynamical behavior of the proton can be depicted from these characteristics and their adjustment to the water molecules or the surrounding ions. Further, new pathways open up in which protonated nucleobases are generated by proton transfer from the ionized water molecules and elimination of a hydroxyl radical in this review, the analysis will be focused on understanding the mechanism of untargeted mutations in canonical, wobble, Hoogsteen pairs, and mutagenic tautomers through the non-covalent interactions. Further, rare tautomer formation through the single proton transfer (SPT) and the double proton transfer (DPT), quantum tunneling in nucleobases, radiation-induced bystander effects, role of water in proton transfer (PT) reactions, PT in anticancer drugs-DNA interaction, displacement and oriental polarization, possible models for mutations in DNA, genome instability, and role of proton transfer using kinetic parameters for RNA will be discussed.

Complexes of DNA bases and Watson-Crick base pairs interaction with neutral silver Agn (n = 8, 10, 12) clusters: a DFT and TDDFT study.

We study the binding of the neutral Agn (n = 8, 10, 12) to the DNA base-adenine (A), guanine (G) and Watson-Crick -adenine-thymine, guanine-cytosine pairs. Geometries of complexes were optimized at the DFT level using the hybrid B3LYP functional. LANL2DZ effective core potential was used for silver and 6-31 + G** was used for all other atoms. NBO charges were analyzed using the Natural population analysis. The absorption properties of Agn-A,G/WC complexes were also studied using time-dependent density functional theory. The absorption spectra for these complexes show wavelength in the visible region. It was revealed that silver clusters interact more strongly with WC pairs than with isolated DNA complexes. Furthermore, it was found that the electronic charge transferred from silver to isolated DNA clusters are less than the electronic charge transferred from silver to the Agn-WC complexes. The vertical ionization potential, vertical electron affinity, hardness, and electrophilicity index of Agn-DNA/WC complexes have also been discussed.

Theoretical studies of optoelectronic, magnetization and heat transport properties of conductive metal adatoms adsorbed on edge chlorinated nanographenes.

The electronic structures, magnetization and quantum transport properties of edge chlorinated nanographenes (Cl NGRs) (C1-C3) functionalized with conductive metal adatoms (Al, Au and Cu) has been investigated by means of density functional theory (DFT) with periodic boundary conditions and plane wave basis functions. The adsorption energy results depict weak chemisorption and strong physisorption for Au adsorption for C1, while C2 and C3 show strong chemisorption towards the studied metals. The role of dispersion forces has also been studied with an empirical classical model. The results show that the metal clusters avoid hollow sites on the Cl NGRs surface and favor atop and bond sites. The net magnetic moment of 0.73 µ B is observed for the (Cl NGRs-metals) system and is in reasonable agreement with the previous calculations carried out on graphene nanoribbons. The TDDFT calculations predict that the absorption spectra for metal dimer-Cl NGRs lie in the visible region. The predictive electrical conductivity of these systems suggests that the metal adatoms play an important role in the transport properties of devices and can be used for thermoelectric applications.

The effect of substituted 1,2,4-triazole moiety on the emission, phosphorescent properties of the blue emitting heteroleptic iridium(III) complexes and the OLED performance: a theoretical study.

A series of neutral heteroleptic mononuclear iridium(III) complexes was investigated using the density functional theory/time-dependent density functional theory approach to determine the effect of the substituted 1,2,4-triazole moiety on the electronic structures, emission, and phosphorescent properties and the organic light emitting diode (OLED) performance. The results reveal that substitution of the free position in the triazole ring by -PhOCH3 (2) provides a higher emission energy and a lower oscillator strength, leading to longer radiative lifetime values mainly due to the ligand-to-ligand charge transfer transition character. The evaluation, based on one-center spin-orbit coupling, results in higher kr values for the substituent -F5Ph (5) and a lower ΔE(S-T) value. Furthermore, we also investigated the performance of the OLED device, including the charge injection/transport/balance ability, increases in the Förster energy transfer rate and triplet exciton confinement for host and guest materials of blue emitting Ir(III) complexes. Finally, we hope that our investigations will help in the design of highly efficient phosphorescent materials.